{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 盒图不在关注是均值和权值了，关注四分位和中位数，用户观察数据离群点【查看盒图图片定义】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'box_plot')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAGGCAYAAACuZyKVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa584400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=[np.random.normal(0,std,100) for std in range(1,4)]# 构造三个正太分布的数据\n",
    "fig=plt.figure(figsize=(8,6))\n",
    "boxplt=plt.boxplot(df,notch=False,sym='s',vert=True)  #notch:是否是基本图形，false:基本图形,sym：离群点用什么表示，vert：横着还是竖着\n",
    "#画标尺：\n",
    "plt.xticks([y+1 for y in range(len(df))],['x1','x2','x3'])\n",
    "plt.xlabel(\"box_x\",fontsize=12)\n",
    "plt.title(\"box_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tSX6jN3w4yV8mOe0w4+zp7rXuXtu5c+e0jwI4rNXV1VTVPS6rq6uLnhqwibne/JXkHUku\nSnJNVZ2d5OQkt809K2Ay+/fvzyaLWKmqBcwGOJJ5w/zmJG+uqk8kuSPJCzZbxgYAjs5cYe7uO5L8\n4ERzAYCl55u/AGAgwgwAA5n3NWZgcCsrK5u+0WtlZWUBswGORJhhm9u3b9+ipwAcA0vZADAQYQaA\ngQgzAAxEmAFgIMIMAAMRZgAYiDADwECEGQAGIswAMBBhBoCBCDMADESYAWAgwgwAAxFmABiIMAPA\nQIQZAAYizAAwEGEGgIEIMwAMRJgBYCDCDAADEWYAGIgwA8BAhBkABiLMADAQYQaAgQgzAAxEmAFg\nIMIMAAMRZgAYiDADwECEGQAGsmOeX66qxyV5Y5IHJLkzyT/p7g9PMTEAFmTv+vHb/oK1Yxt7Cc0V\n5iSvTfLT3f2/quqZs+sXzj0rABZHPBdq3qXsTvLg2c/flOTWOccDgKU27xHzjyd5b1W9LhuRf/Lh\nNqyq3Ul2J8muXbvm3C0AbE9HDHNVXZ3kEZvcdWmSpyX559399qp6XpLLk1y82TjdvSfJniRZW1vr\n+zxjANjGjhjm7t40tElSVb+c5KWzq7+e5E0TzQsAltK8rzHfmuSC2c8XJfn0nOMBwFKb9zXmFyf5\nharakeTPM3sNGQC4b+YKc3f/TpInTjQXAFh6vvkLAAYizAAwEGEGgIEIMwAMRJgBYCDCDAADqe4T\n/+2YVXUgyf4TvuOt57Qkty16EmwrnlNMzXPq6Kx0986j2XAhYeboVNV6dzv/GpPxnGJqnlPTs5QN\nAAMRZgAYiDCPbc+iJ8C24znF1DynJuY1ZgAYiCNmABiIMA+uqt5TVV+pqncvei5sbVX1uKr6/aq6\nsar+oKr+/qLnxNZWVStVdV1V3TB7Xv3ooue0HVjKHlxVPS3JNyb5R939rEXPh62rqs5O0t396ao6\nPcl1Sb6zu7+y4KmxRVXVydnoyO1VdUqSTyR5cnffuuCpbWmOmAdRVefNjmIeUFUPmv3r89zu/kCS\nry56fmwtmz2fkpzc3Z9Oktkfzi8kOaovPIDDPKfO7u7bZ5vcP5oyiR2LngAbuvsjVXVVklcneWCS\nX+nuTyx4WmxRR3o+VdWTkpyc5P8uaIpsMYd7TlXVWUn+Z5JvS/IvHS3Pz1L2QGbLQh9J8ufZWA66\na3b7hUleZimbY3Evz6dvTXJNkhd094cWN0O2msM9p2b3nZ7kHUme3d1/sqApbguWHcby0CSnJDk1\nyQMWPBe2vns8n6rqwdk4unmVKHMfHPZv1OxI+cYkT1nAvLYVYR7LniSXJbkyyb9f8FzY+r7u+TQ7\n2vnNJL/c3b++0JmxVR36nDqzqh6YJFX1zUnOT/KpBc5vW/Aa8yCq6oeS3Nndb62qk5L8XlVdlOSn\nkzwmySlVdUuSH+nu9y5yroxvs+dTkkuSPDXJw6rqhbNNX9jdNyxommwhh3lO/Y0kP1dVnaSSvK67\nP77IeW4HXmMGgIFYygaAgQgzAAxEmAFgIMIMAAMRZgAYiDADwECEGbaQqtpXVRcveh7A8SPMADAQ\nYQaAgQgzbD3nVdVNVfXlqrqiqu4+QcWLq+ozVfWlqrpqdrafVNWTq+q22en5UlWPraqvVNVjDreD\nqnr0bJwnzK6fPhvjwhPw+GCp+UpO2EKqal+SryV5RpI/TfKuJL+d5INJfi3J383GGX5el+Sx3f3U\n2e/9TJLvSfJ9Sa5Nsqe733CEfb04yU8keWI2Tn7x8e5+2fSPCjiYMMMWMgvza7r7jbPrz0zyi9k4\nv/IXu/vls9tPSfLlJN/e3fuq6n5JPpTk5CSfS/KMPor/+avqqiSPTNJJzuvu2yd/UMDXsZQNW8/N\nB/28P8nps8v+u2/s7q8l+WKSM2bX/yLJW5Kcm+T1RxPlmf86+51fFGU4MYQZtp6zDvp5V5JbZ5eV\nu2+sqgcleVg2jo5TVWck+ckkVyR5fVXd/0g7mR11/3ySy5P8VFU9dKoHAByepWzYQmZL2V/NxmvM\nf5bknUn+d5IPJHlbku9N8skkr03yxO7+21VVSd6X5Pokr0jyniQfu3vZ+172dXmSU7v7eVW1J8lD\nuvt5x+WBAX/FETNsPW/NRmg/O7u8urs/kOSyJG9P8vkkj05yyWz7H0vyLUkumy1hvyjJi6rqKYfb\nQVU9N8nTk/zo7KafSPKEqnr+9A8HOJgjZgAYiCNmABjIjkVPAFiMqtqV5KbD3H1Od//RiZwPsMFS\nNgAMxFI2AAxEmAFgIMIMAAMRZgAYiDADwED+P+S5ZEFH31+LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7871d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 如何设置设置线条的颜色\n",
    "df=[np.random.normal(0,std,100) for std in range(1,4)]# 构造三个正太分布的数据\n",
    "fig=plt.figure(figsize=(8,6))\n",
    "boxplt=plt.boxplot(df,notch=False,sym='s',vert=True)  #notch:是否是基本图形，false:基本图形,sym：离群点用什么表示，vert：横着还是竖着\n",
    "#画标尺：\n",
    "plt.xticks([y+1 for y in range(len(df))],['x1','x2','x3'])\n",
    "plt.xlabel(\"box_x\",fontsize=12)\n",
    "plt.title(\"box_plot\")\n",
    "# 如何获取boxplot的组件操作\n",
    "for components in boxplt.keys():\n",
    "     for line  in boxplt[components]:\n",
    "            line.set_color('pink')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'box_plot')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XJLksyfFJ3tLdN6/08VV1UZKLkmTHjh0bMyQMyOeXH5ifD6PZ8KDPvSrJ9Um+kuTFizyw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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9176748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 如何横着画盒图\n",
    "df=[np.random.normal(0,std,100) for std in range(1,4)]# 构造三个正太分布的数据\n",
    "fig=plt.figure(figsize=(8,6))\n",
    "boxplt=plt.boxplot(df,notch=False,sym='s',vert=False)  #notch:是否是基本图形，false:基本图形,sym：离群点用什么表示，vert：横着还是竖着\n",
    "#画标尺：\n",
    "plt.yticks([y+1 for y in range(len(df))],['x1','x2','x3'])\n",
    "plt.ylabel(\"box_x\",fontsize=12)\n",
    "plt.title(\"box_plot\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'box_plot')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZVEl/klRP0uPu/kHMZSFDVbY/JS84s5ukpTGViAxT0T5lZk0kvSjpEEnXMlpO\nHVPZAUlOC72rsp+edXL3jcnnT5R0DVPZ2BHb2Z8OlPSGpL7uPju+CpFpKtqnkq81kjRZUg93/zKm\nEmsEph3Cso+k3SXtIaluzLUg822zP5nZniob3dxEKGMnVPgdlRwpL5R0fAx11SgEc1hGSbpZ0hOS\n7oy5FmS+n+1PydHOc5LGufvTsVaGTLX1PtXYzOpJkpntLelYcYGilHGMORBmdrGkDe7+pJnVkvSO\nmXWRNEhSS0m7m9lySf3cfXqctSJ85e1Pks6X1FnSvmb2m+Smv3H392IqExmkgn2qtaRhZuYqu8rg\n3e7+fpx11gQcYwYAICBMZQMAEBCCGQCAgBDMAAAEhGAGACAgBDMAAAEhmAEACAjBDGQQMys2s1Pi\nrgNA+hDMAAAEhGAGACAgBDOQeY4ys0Vm9q2ZjTGzzReouNTMPjazlWY2NXm1H5lZJzP7Onl5PpnZ\nEWa2ysxaVtSBmTVLttMu+bhRso0Tq+HzAVmNU3ICGcTMiiWtlnS6pH9Lel7S65JekzRRUleVXeHn\nbklHuHvn5PsGS+oo6QxJcySNcvf7K+nrUklXSWqvsotfvO/u10T/qQBsiWAGMkgymIe6+0PJx90l\n3aey6yt/4+79k8/vLulbSYe6e7GZ7SpptqTdJH0u6XSvwj9+M5sqqakkl3SUu6+N/EMB+BmmsoHM\n89kW90skNUreSjY/6e6rJX0j6aDk4/WSHpXURtLwqoRy0v9Lvuc+QhmoHgQzkHmabHH/YEkrkre8\nzU+aWQNJ+6psdCwzO0jSQEljJA03szqVdZIcdf+vpNGSbjWzfaL6AAAqxlQ2kEGSU9n/Utkx5jWS\npkh6W9KrksZLOlXSh5LuktTe3Y8zM5P0V0l/kzRA0jRJ8zdPe2+nr9GS9nD33mY2StJe7t47LR8M\nwE8YMQOZ50mVBe2y5O1P7v6qpJslPSvpn5KaSTo/uf0Vkg6QdHNyCrtAUoGZHV9RB2bWU9Jpkn6X\nfOoqSe3M7MLoPw6ALTFiBgAgIIyYAQAISO24CwAQDzM7WNKiCl5u5e6fVmc9AMowlQ0AQECYygYA\nICAEMwAAASGYAQAICMEMAEBACGYAAALyfy2ukpoeA5BZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa881588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 盒图变形notch 设置诶True\n",
    "df=[np.random.normal(0,std,100) for std in range(1,4)]# 构造三个正太分布的数据\n",
    "fig=plt.figure(figsize=(8,6))\n",
    "boxplt=plt.boxplot(df,notch=True,sym='s',vert=True)  #notch:是否是基本图形，false:基本图形,sym：离群点用什么表示，vert：横着还是竖着\n",
    "#画标尺：\n",
    "plt.xticks([y+1 for y in range(len(df))],['x1','x2','x3'])\n",
    "plt.xlabel(\"box_x\",fontsize=12)\n",
    "plt.title(\"box_plot\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAGGCAYAAACuZyKVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPwG0nD+9PovjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa527f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 利用颜色填充\n",
    "df=[np.random.normal(0,std,100) for std in range(1,4)]# 构造三个正太分布的数据\n",
    "fig=plt.figure(figsize=(8,6))\n",
    "boxplt=plt.boxplot(df,notch=False,sym='s',vert=True,patch_artist=True) #patch_artist: 是否画出line2D效果\n",
    "#画标尺：\n",
    "plt.xticks([y+1 for y in range(len(df))],['x1','x2','x3'])\n",
    "plt.xlabel(\"box_x\",fontsize=12)\n",
    "plt.title(\"box_plot\")\n",
    "colors=['pink','lightblue','lightgreen']\n",
    "for patch,color in zip(boxplt['boxes'],colors):\n",
    "    patch.set_facecolor(color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 小提琴图violinplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#通过violin_plot 画出来盒子图可以看到可以看到值分分布情况[从两个图观察查看密度分布和离群值]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.XTick at 0xc5eef60>,\n",
       " <matplotlib.axis.XTick at 0xc3e9b38>,\n",
       " <matplotlib.axis.XTick at 0xc604da0>,\n",
       " <matplotlib.axis.XTick at 0xd6a3668>,\n",
       " Text(0,0,'x1'),\n",
       " Text(0,0,'x2'),\n",
       " Text(0,0,'x3'),\n",
       " Text(0,0,'x4'),\n",
       " <matplotlib.axis.XTick at 0xd62fe48>,\n",
       " <matplotlib.axis.XTick at 0xd610c88>,\n",
       " <matplotlib.axis.XTick at 0xd61ad68>,\n",
       " <matplotlib.axis.XTick at 0xd69bf60>,\n",
       " Text(0,0,'x1'),\n",
       " Text(0,0,'x2'),\n",
       " Text(0,0,'x3'),\n",
       " Text(0,0,'x4')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j1S5005OTG00cuHCisPdLy9X8cksVFbityCwnYPvJsJsOPicXBYihK6simdXI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\noPrPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc55beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(nrows=1,ncols=2,figsize=(8,6))\n",
    "df=[np.random.normal(0,std,100) for std in range(6,10)]# 构造三个正太分布的数据\n",
    "axes[0].violinplot(df,showmeans=False,showmedians=True)\n",
    "axes[0].set_title(\"voilin plot\")\n",
    "\n",
    "\n",
    "axes[1].boxplot(df)\n",
    "axes[1].set_title(\"box plot\")\n",
    "\n",
    "#如何设置网格\n",
    "for ax in axes :\n",
    "    ax.yaxis.grid(True)\n",
    "    ax.set_xticks([y+1 for y in range(len(df))])\n",
    "plt.setp(axes,xticks=[y+1 for y in range(len(df))],xticklabels=['x1','x2','x3','x4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
